Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Data Driven Inverse Design of Optical Metamaterials

  The unique optical properties of metamaterials hold the potential to revolutionize fields like displays, imaging, and sensing. However, unlocking this potential requires efficient inverse design algorithms capable of accurately predicting the complex structures that yield desired optical responses. Traditional methods struggle with the vast design space and computational demands of complex metamaterials. To address these challenges, we introduce prior knowledge into deep learning (DL) frameworks for accurate and scalable inverse design of optical metamaterials. This carefully selected prior knowledge reduces data requirements and enhances model robustness. We integrate the prior knowledge as (a) governing physics equations for simplified structures and (b) learned parameters from DL models trained on simpler designs. Focusing on plasmonic metamaterials with metal gratings on a dielectric film, we consider two designs: (a) simple periodic metal blocks and (b) graded metal blocks with varying widths. Our developed DL models achieve impressive performance: 97.4% accuracy for predicting design parameters in simple blocks, 86% accuracy for diffraction efficiencies in graded blocks, and 97% accuracy for spatial electric field distributions. Furthermore, we develop an auto-regressive transformer-based DL model for the inverse design of adaptive metamaterials with dynamic components. This model captures the complex relationships between dynamic optical responses and the metamaterial's design, including both static and time-varying elements. It achieves 98.5% accuracy for material prediction, 82.5% for grating profile design, and 95% accuracy for dynamic thickness prediction. We also apply our DL expertise to a critical real-world challenge: the inverse prediction of blood constituent concentrations for sample integrity in medical diagnostics. To overcome the limitations of obtaining extensive real blood samples, we develop a physics-based forward model that simulates the spectral response of blood samples under various conditions. This model generates the data needed to train a robust DL model for accurately predicting hemoglobin and bilirubin concentrations. Our DL model achieves an accuracy of up to 99% for hemoglobin and bilirubin concentration prediction, ensuring reliable sample integrity analysis for medical diagnostics.</p

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%

Does Skeletal Maturity Predict the Pattern of Tibial Tubercle Avulsion Fracture?

BACKGROUND: Proximal tibial physeal development and closure is thought to relate to tibial tubercle avulsion fracture (TTAF) patterns. Prior work has yet to formally evaluate the relationship between skeletal maturity and fracture pattern.  Using 2 knee radiograph-derived skeletal maturity assessments [growth remaining percentage (GRP) and epiphyseal union stage], we examined their association with TTAF injury patterns using the Ogden and Pandya fracture classifications. We hypothesized that different TTAF injuries would occur during unique periods of skeletal development. METHODS: Pediatric patients sustaining TTAFs treated at a single institution (2008-2022) were identified using diagnostic and procedural coding. Demographics and injury characteristics were collected. Radiographs were reviewed to assign epiphyseal union stage, Ogden and Pandya classifications and for measurements to calculate GRP. Univariate analyses examined the relationship between injury subgroups, patient demographics, and skeletal maturity assessments. RESULTS: Inclusion criteria identified 173 patients with a mean age of 14.76 (SD: 1.78) and 2.95% (SD: 4.46%) of growth remaining. The majority of injuries were classified Ogden III/Pandya C. Most (54.9%) were the result of the axial loading mechanism. Ogden groups showed no significant differences across all patient characteristics studied including age and GRP. With the exception of Pandya A fractures, we did not identify a direct relationship between GRP, age, and Pandya groups. Epiphyseal union stage differed for Pandya A and D groups. CONCLUSIONS: A predictable pattern in TTAF characteristics across skeletal (GRP), epiphyseal union, or chronologic age was not identified in this study. Distal apophyseal avulsions (Ogden I/II and Pandya A/D) occurred across a broad chronologic and skeletal age range. No differences were identified in epiphyseal or posterior extension (Ogden III/IV and Pandya B/C) injuries. Although differences in age and GRP were identified among Pandya As, this is thought to be due to the degree of skeletal immaturity that is a prerequisite for differentiation from Pandya Ds. LEVEL OF EVIDENCE: Level III-retrospective cohort study

Mechanical response at peri-implant mandibular bone for variation of pore characteristics of implants: A Finite Element Study

In this paper, the mechanical response of generic dental implants having calculated porosities with varying pore-sizes has been evaluated. The purpose of this study was to compare the developed stress-strain of designed porous implants (i.e., stress at the implant and strain at the peri-implant bone) with that of the non-porous implant. Methods: 3D model of a mandible was prepared from CT scan data and nine generic dental implant models have been designed having 10%, 20%, and 30% porosity with 500, 700, and 900 micron pore size along with a non-porous model for carrying out FE analyses. First, failure analyses of implants, under a biting force of 250 N have been performed. Next, the remaining implants have been further evaluated under average compressive chewing load of 100 N, for mechanical responses at bone-implant interface. Results: Von Mises strain at the peri-implant mandibular bone increases with the increase in percentage porosity of the implant material and maximum implant stress remained much below the yield stress level. Conclusion: Implant stiffness and compressive strength vary as a function of porosity and pore size. Strain obtained on the peri-implant bone is sufficient enough to facilitate better bone growth with the 700 micron pore size and 30% porosity, thus reducing the effect of stress shielding

Cellular landscaping of cisplatin resistance in cervical cancer

Funding Information: This research was funded by “Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI) del Gobierno de Canarias” (project ProID2020010134 ), and CajaCanarias (project 2019SP43 ). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Publisher Copyright: © 2022 The AuthorsCervical cancer (CC) caused by human papillomavirus (HPV) is one of the largest causes of malignancies in women worldwide. Cisplatin is one of the widely used drugs for the treatment of CC is rendered ineffective owing to drug resistance. This review highlights the cause of resistance and the mechanism of cisplatin resistance cells in CC to develop therapeutic ventures and strategies that could be utilized to overcome the aforementioned issue. These strategies would include the application of nanocarries, miRNA, CRIPSR/Cas system, and chemotherapeutics in synergy with cisplatin to not only overcome the issues of drug resistance but also enhance its anti-cancer efficiency. Moreover, we have also discussed the signaling network of cisplatin resistance cells in CC that would provide insights to develop therapeutic target sites and inhibitors. Furthermore, we have discussed the role of CC metabolism on cisplatin resistance cells and the physical and biological factors affecting the tumor microenvironments.Peer reviewe

Global Burden of Cardiovascular Diseases and Risks, 1990-2022

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is a multinational collaborative research study with >10,000 collaborators around the world. GBD generates a time series of summary measures of health, including prevalence, cause-specific mortality (CSMR), years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) to provide a comprehensive view of health burden for a wide range of stakeholders including clinicians, public and private health systems, ministries of health, and other policymakers. These estimates are produced for 371 causes of death and 88 risk factors according to mutually exclusive, collectively exhaustive hierarchies of health conditions and risks. The study is led by a principal investigator and governed by a study protocol, with oversight from a Scientific Council, and an Independent Advisory Committee.1 GBD is performed in compliance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER).2 GBD uses de-identified data, and the waiver of informed consent was reviewed and approved by the University of Washington Institutional Review Board (study number 9060). This almanac presents results for 18 cardiovascular diseases (CVD) and the CVD burden attributed to 15 risk factors (including an aggregate grouping of dietary risks) by GBD region. A summary of methods follows. Additional information can be found online at https://ghdx.healthdata.org/record/ihme-data/cvd-1990-2022, including:Funding was provided by the Bill and Melinda Gates Foundation, and the American College of Cardiology Foundation. The authors have reported that they have no relationships relevant to the contents of this paper to disclose. The contents and views expressed in this report are those of the authors and do not necessarily reflect the official views of the National Institutes of Health, the Department of Health and Human Services, the U.S. Government, or the affiliated institutions